Unclear exactly what you're asking. The linked paper describes an algorithm (patent status unclear). That paper happens to link to a GPL licensed implementation whose authors explicitly solicit business licensing inquiries. The related model weights are available on Hugging Face (license unclear). Notably the HF readme file contains conflicting claims. The metadata block specifies apache while the body specifies GPL.
The paper says it is based on YOLOv8, which uses the even stricter AGPL-3.0. That means you can use it commercially, but all derived code (even in a cloud service) must be made open source as well.
They probably mean the algorithm, but nevertheless the YOLO models are relatively simple so if you know what you're doing it's pretty easy to reimplement them from scratch and avoid the AGPL license for code. I did so once for the YOLOv11 model myself, so I assume any researcher worth their salt would also be able to do so too if they wanted to commercialize a similar architecture.
You don't just need to reimplement the architecture (which is trivial even for non-researcher level devs), you need to re-train the weights from scratch. According to the legal team behind Yolo, weights (including modifications via fine tuning) fall under the AGPL as well and you need to contact their sales team for a custom license if you want to deviate from AGPL.
At least for the Ultralytics YOLO models this is also relatively easy (I've done it too). These models are tiny by today's standards, so training them from scratch even on consumer hardware is doable in reasonable time. The only tricky part is writing the training code which is a little more complicated than just reimplementing the architecture itself, but, again, if a random scrub like me can do it then any researcher worth their salt will be able to do it too.
AFAIK their pretrained models just use publicly available datasets. From their README:
> YOLO11 Detect, Segment and Pose models pretrained on the COCO dataset are available here, as well as YOLO11 Classify models pretrained on the ImageNet dataset.
I assume they refer to the academic basis for the algorithm rather than the implementation itself.
Slightly unrelated, how does AGPL work when applied to model weights? It seems plausible that a service could be structured to have pluggable models on the backend. Would that be sufficient to avoid triggering it?
The standard for humans is a clean room reimplementation so I guess you'd need 2 AIs, one to translate A into a list of requirements and one to translate that list back into code.
But honestly by the time AI is proficiently writing large quantities of code reliably and without human intervention it's unclear how much significance human labor in general will have. Software licensing is the least of our concerns.
Presumably depends on the impacts. It's an ideology that seeks user freedom. If you need access to the source code to use as a template that clearly favors proprietary offerings. But if you can easily clone proprietary programs that would favor the end user.
In "HTML Snake" the video cuts just as the snake intersects with the obstacle. Presumably because the game crashed (I can't see endGame defined anywhere)
This video is featured in the main announcement so it's kinda dishonest if you ask me.
Seeing this makes me wonder if they have frontend \ backend engineers working on code, because they are selling the idea that the machine can do all that, pretty hypocritical for them if they do have devs for these roles.
Thanks for this. We had previously evaluated n8n a year ago and couldn’t use it because it wasn’t open source and they wanted to charge us $50k/yr. I wasn’t aware of ActivePieces, will check it out.
Thank you for this recommendation. I was looking for a FOSS replacement for n8n with a similar user interface and feature set, and Activepieces seems to be a great fit.
Its the latter. The project does good work so don't want to dump on the project.
When one sees source code on Github, one assumes that its OSS, but its not and thats why I share that.
The license is a fair code license and it says "is commercially restricted by its authors" and its not clear what commercially restricted really means here.
Thanks for doing this. I found n8n 2 days ago and was working on integrating it but just found that their "license" is too much. I was going to integrate it on my platform and sell their product as an affiliate but that license is a no-go.
This is so nostalgic. I actually met my cofounder on github due to a discussion on twisted vs gevent back in 2011. I had my inital code in twisted and he wrote the gevent piece. Fast forward 12 years and we still use gevent at http://plivo.com :)
Off topic: PLIVO, the norwegian term actually is a protocol used by critical services here. Thought you might find it interesting :)
> PLIVO (an abbreviation for ongoing life-threatening violence) is a procedure for cooperation between the police, the fire service, the rescue service and the healthcare system in incidents where life-threatening violence is perpetrated against several people.
I shared my exp below on one of the comments, sharing here too - I think overall the quality is significantly poorer on GPT4 with plugins and bing browsing enabled. If you disable those, I am able to get the same quality as before. The outputs are dramatically different. Would love to hear what everyone else sees when they try the same.
I have some first hand thoughts. I think overall the quality is significantly poorer on GPT4 with plugins and bing browsing enabled. If you disable those, I am able to get the same quality as before. The outputs are dramatically different. Would love to hear what everyone else sees when they try the same.
No, while I have no hard data, the experienced quality of the default GPT-4 model feels like it has gone down tremendously for me as well. Plugins and Bing browsing have so far for me almost never worked at all. I retry these just once a week but there always seem to be technical issues.
Founder of https://www.plivo.com here. We have seen similar patterns of fraud on our customers primarily in the international markets, outside the US & Canada. It typically happens on repeating number ranges that are sometimes not even in service. MaxPrice approach did not work well based on our experience as this would lead to just blocking certain destinations completely. Alternatively, what we found better was have a geo permissions related options where customers could block destinations that are never used at a network level and additionally introduce rate limits for those networks, so its not open to an attack. Plivo's console screenshot here: https://www.dropbox.com/s/kbw3l0oyw7fcjmr/plivo_console_sms_...
pardon my ignorance - what exactly is involved in reimplementing these models?
i assume there's only a superficial description of the architecture, and no weights to load in, so you'll have to train everything from scratch? do we even have their dataset?
Generally it's without weights, but MusicLM is also a WIP. More mature implementations have descriptions on how to train them and follow ups on small scale/crowd-sourced experiments & research[1].
